Luis Villarrubia and Alex Acero
In this paper we describe a technique for non-keyword rejection and we will evaluate in the context of an audiotex service using the ten Spanish digits. The baseline keyword recognition system is a speaker-independent continuous density Hidden Markov Model recognizer. We propose the use of an affine transformation to the log-probability of the garbage model, an HMM model trained to account for both nonkeyword speech and non-stationary telephone noises. The parameters of the transformation for the case of isolated keywords are chosen to minimize a cost function that weighs the keyword error rate, keyword rejection rate and false acceptance rate according to the a priori probabilities of keywordhon-keyword and the requirements of the specific application. This technique was also extended to embedded keywords (word-spotting). Use of this rejection technique on the audiotex application reduced the total cost function up to 20% for isolated-word case and 12% for the word-spotting case.
|Published in||Proc. of the International Conference on Acoustics, Speech and Signal Processing|
|Publisher||Institute of Electrical and Electronics Engineers, Inc.|
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